We simulated markets consisting of 1000 agents that decided sequentially which of 100 alternatives to choose, after searching the market according to public popularity information. We systematically varied the diversity of preferences in the market, \(d\) , by setting the subjective variance of each individual agent \(\sigma ^2_{s}\)  equal to \(d \in \lbrace 0, 0.1,...,0.9,1 \rbrace  \)  and the variance of the objective utility \(\sigma ^2_{o} \)  equal to \( 1 - d \) . We drew subjective utilities for each agent independently, whereas the draw of the objective utilities was the same for all agents. Next, we varied the cost of search, \( c \) , that agents have to pay when sampling an alternative, \(c = \lbrace  1/2^2, 1/2^3,1/2^4, 1/2^5, 1/2^6 , 1/2^7, 1/2^8 \rbrace \) . These costs correspond to the threshold parameters \( T = \lbrace  0.34, 0.78 , 1.15 , 1.47, 1.76 , 2.03, 2.28 \rbrace  \) ; these parameters are optimal for a random search model [1]}, which our agents used despite searching according to the popularity ranking. This yields \(11 \times 7 = 77\)  markets, each of which was simulated 1000 times to obtain stable averages.The code for the simulations can be openly accessed at https://osf.io/up5pe/
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